Old-Growth Attributes Prediction in the Coastal Western Hemlock Ecosystem, British Columbia using LiDAR
Bibliographic record
Abstract
Old-growth forests have complex structure variability that provides critical habitat for endangered species, enhancing biodiversity and ecosystem services, but old-growth has become rare due to historical forest harvesting. In order to support the conservation of Old-Growth forests in B.C., it is necessary to identify old-growth forests from other non-old-growth forests. However, the traditional age measurement methods are costly and intractable at landscape scales, also the structural characteristics of old growth are not included. This study attempts to predict the distribution of old-growth attributes in the Coastal Western Hemlock (CWH) zone in British Columbia using area-based lidar metrics. Lidar point clouds of 61 forestry inventory plots are extracted to generate liDAR metrics to create multilinear regression models for four old-growth attributes: standard deviation of diameter at breast height (DBH), maximum tree DBH, average live crown percentage, and the sum of understory plants percentage. The results show that multilinear regression and LiDAR data can be used to estimate the distribution of old-growth attributes except for the average live crown percentage. An old-growth index is derived from four old-growth attributes for mapping the potential locations of old-growth. However, the validation results of 11.28% from vegetation resource inventory (VRI) illustrate that the old-growth index does not successfully identify old growth. Despite the challenges encountered, the prediction results can still be used to identify old-growth attributes and enhance knowledge of old-growth landscapes. Also, this study has potential applications in old-growth forest restoration in the Western Hemlock Ecosystem and supports the old-growth management plan of the government.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".